TSR Ablations.
For the Indoor dataset, we conducted several ablation experiments on our Transformer Structure Restoration (TSR), and the results are displayed in Tab. REF  and Tab. REF . As illustrated in Tab. REF  and the first two rows of Tab. REF , replacing one standard self-attention module [1]} with an axial attention module [2]} in our Transformer Block can greatly reduce the GPU memory usage and speed up the model inference while keeping all metrics basically unchanged. Furthermore, we add the relative position encoding (RPE) [3]} into our axial attention module, which can boost our results. Note that the RPE must be incorporated with the axial attention module in row-wise and column-wise, while standard attention based RPE costs much more GPU memory due to the long sequence. On the other hand, as we think that a higher recall will benefit the later image inpainting, we further multiply the line logits by 4 before feeding it through the sigmoid activation function in all the experiments. This strategy enhances recall while only compromising a little precision.
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